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Automated Retinal Blood Vessel Segmentation Using Modified U-Net Architecture

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Proceedings of the 4th International Conference on Communication, Devices and Computing (ICCDC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1046))

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Abstract

Robotized tracking of vein structures is becoming a crucial aspect for better analysis of vascular ailment. Diabetes is an internationally predominant illness. The retinal images of diabetic patients are used for determining the severity level. This work utilizing profound learning procedure could significantly benefit in effective identification. In spite of the fact that we utilize just a little part of pictures (1/4) in preparing however are helped with higher picture goals. An essential aspect in determining the existence of many eye disorders and heart issues is the status of the blood vessels in the retina. The segmentation of blood vessels in fundus pictures has become quite popular for this reason. This study suggests a method for segmenting blood vessels using a modified U-net architecture. These outcomes propose that a profound learning framework could expand the expense adequacy of screening.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Haldia Institute of Technology, Haldia, West Bengal, 721657, India

    Debasis Maji

  2. Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India

    Souvik Maiti & Gautam Sarkar

  3. Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, 713209, India

    Ashis Kumar Dhara

Authors
  1. Debasis Maji
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  2. Souvik Maiti
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  3. Ashis Kumar Dhara
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  4. Gautam Sarkar
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Corresponding author

Correspondence to Debasis Maji .

Editor information

Editors and Affiliations

  1. Department of Applied science, Aluminum Research Center REGAL, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada

    Dilip Kumar Sarkar

  2. Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

    Pradip Kumar Sadhu

  3. Department of Electronics and Communication Engineering, Central Institute of Technology, Kokrajhar, Assam, India

    Sunandan Bhunia

  4. Department of Electronics and Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Jagannath Samanta

  5. Department of Electronics and Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Suman Paul

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Maji, D., Maiti, S., Dhara, A.K., Sarkar, G. (2023). Automated Retinal Blood Vessel Segmentation Using Modified U-Net Architecture. In: Sarkar, D.K., Sadhu, P.K., Bhunia, S., Samanta, J., Paul, S. (eds) Proceedings of the 4th International Conference on Communication, Devices and Computing. ICCDC 2023. Lecture Notes in Electrical Engineering, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-99-2710-4_3

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  • DOI: https://doi.org/10.1007/978-981-99-2710-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2709-8

  • Online ISBN: 978-981-99-2710-4

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